---
title: "Flex Dashboards for Sharing Results"
output:
  flexdashboard::flex_dashboard:
    source_code: embed
    orientation: columns
    theme: cosmo
editor_options: 
  chunk_output_type: console
---

```{r, libraries, include = FALSE}
## here to navigate project directory
library(here)

## tidyverse for data wrangling and visualization
library(tidyverse)

## snakecase for naming conventions
library(snakecase)

## flexdashboard to build dashboards in R Markdown
library(flexdashboard)

## shiny for reactive dashboards
library(shiny)

## jsonlite to import json data files
library(jsonlite)

## hexbin for hex plots
library(hexbin)

## DT for web-friendly, interactive tables
library(DT)
```

```{r, global, include = FALSE}
### import data objects
## use read_json() to import the data file
airline <- read_json(
  ## use here() to locate file in our project directory
  here(
    # folder
    "data", 
    # file
    "airline.json"
  ),
  ## turn into data table
  simplifyVector = TRUE
)

### rename variables, convert variables
## overwrite 
airline <- airline %>%
  ## convert to tibble
  as_tibble() %>%
  ## mutate variable types and values
  mutate(
    ## characters to factors
    across(
      # find character variables
      .cols = where(is.character),
      # convert to factors
      .fns = as_factor
    ),
    ## relevel
    Class = fct_relevel(
      # factor
      Class, 
      # level
      "Business", 
      # position
      after = 2
    )
  ) %>%
  ## clean variable names
  rename_with(
    # function
    .fn = to_snake_case
  )
```

```{css, echo = FALSE}
/* adjust height of data tables */
.dataTables_scrollBody {
  /* adjust height */
  max-height: 100% !important;
}
```

```{r, gauge_compute, include = FALSE}
# Create the gauge_compute data table
gauge_compute <- airline %>%
  group_by(travel_type) %>%
  summarize(count = n(),
            mean_recommend = format(round(mean(recommendation), 2), nsmall = 2)) %>%
  ungroup()

# Print the gauge_compute data table
print(gauge_compute)
```

Plots 
================

Column {.tabset .tabset-fade}
-----------------------------------------------------------------------

### Violin Plot

```{r, plot_1}
### violin plot
## call data and mapping
ggplot(
  # data
  airline,
  # mapping
  aes(
    # x-axis
    x = customer_type, 
    # y-axis
    y = recommendation, 
    # fill
    fill = class
  )
) +
  ## violin geometry
  geom_violin() +
  ## labels
  labs(
    # x-axis
    x = "Customer Type", 
    # y-axis
    y = "Recommendation", 
    # fill
    fill = "Class"
  )
```

### Hex Bin Plot 

```{r, plot_2}
### hex plot
## call data and mapping
ggplot(
  # data
  airline,
  # mapping
  aes(
    # x-axis
    x = age, 
    # y-axis
    y = recommendation
  )
) +
  ## hex geometry
  geom_hex() +
  ## labels
  labs(
    # x-axis
    x = "Age", 
    # y-axis
    y = "Recommendation", 
    # fill
    fill = "Count"
  )
```

### Heat Map

```{r, plot_3}
### heat map
## call data and mapping
ggplot(
  # data
  airline,
  # mapping
  aes(
    # x-axis
    x = leg_room, 
    # y-axis
    y = cleanliness, 
    # fill
    fill = recommendation
  )
) +
  ## tile geometry
  geom_tile() +
  ## adjust fill color
  scale_fill_gradient2(
    # low value color
    low = "blue",
    # mid value color
    mid = "white",
    # high value color
    high = "red",
    # midpoint
    midpoint = 50
  ) +
  ## labels
  labs(
    # x-axis
    x = "Leg Room", 
    # y-axis
    y = "Cleanliness", 
    # fill
    fill = "Recommendation"
  )
```

### 2D Density Plot

```{r, plot_4}
### filled 2d density
## call data and mapping
ggplot(
  # data
  airline,
  # mapping
  aes(
    # x-axis
    x = flight_distance, 
    # y-axis
    y = recommendation
  )
) +
  ## tile geometry
  geom_density_2d_filled(
    # contours
    contour_var = "ndensity"
  ) +
  ## facet
  facet_grid(
    # rows by columns
    customer_type ~ class
  ) +
  ## labels
  labs(
    # x-axis
    x = "Flight Distance", 
    # y-axis
    y = "Recommendation", 
    # fill
    fill = "Density"
  )
```

Table 1 {data-navmenu=Tables} 
================

Column
-----------------------------------------------------------------------

### Younger Female Customers

```{r, table_1}
### filtered table
## save
tab_1 <- airline %>%
  ## filter for rows
  filter(
    # condition
    age <= 30,
    # condition
    gender == "Female"
  ) %>%
  ## select columns
  select(
    # filter variables
    age, gender,
    # other characteristics
    customer_type, travel_type:flight_distance,
    # measures
    inflight_wi_fi:recommendation
  )

### display table
## datatable
datatable(
  # data
  tab_1,
  # remove row names
  rownames = FALSE, 
  # filter
  filter = "top",
  # add buttons
  extensions = "Scroller",
  # options
  options = list(
    # table, length, pagination, scrolling
    dom = "tlpS",
    # scroller
    scrollY = "200px",
    # number of rows
    pageLength = 25
  )
)
```

Table 2 {data-navmenu=Tables} 
================

Column
-----------------------------------------------------------------------

### Older Female Customers

```{r, table_2}
### filtered table
## save
tab_2 <- airline %>%
  ## filter for rows
  filter(
    # condition
    age > 30,
    # condition
    gender == "Female"
  ) %>%
  ## select columns
  select(
    # filter variables
    age, gender,
    # other characteristics
    customer_type, travel_type:flight_distance,
    # measures
    inflight_wi_fi:recommendation
  )

### display table
## datatable
datatable(
  # data
  tab_2,
  # remove row names
  rownames = FALSE, 
  # filter
  filter = "top",
  # add buttons
  extensions = "Scroller",
  # options
  options = list(
    # table, length, pagination, scrolling
    dom = "tlpS",
    # scroller
    scrollY = "200px",
    # number of rows
    pageLength = 25
  )
)
```

Gauges
================

Column
-----------------------------------------------------------------------

### Personal Travel Customers

```{r, box_1}

# Create the valueBox
valueBox(
  gauge_compute %>%
  filter(travel_type == "Personal Travel") %>%
  pull(count),
  caption = "Number of Personal Travel Customers",
  icon = icon("chart-bar")
)
```

### Average Recommendation of Personal Travel Customers

```{r, gauge_1}
### gauge
## call function
gauge(
  ## data
  gauge_compute %>%
    ## filter
    filter(
      # condition
      travel_type == "Personal Travel"
    ) %>%
    ## extract value
    pull(mean_recommend),
  ## minimum and maximum
  min = 0, max = 100,
  ## sectors
  sectors = gaugeSectors(
    # green
    success = c(75, 100),
    # orange
    warning = c(50, 74),
    # red
    danger = c(0, 49)
  )
)
```

Column
-----------------------------------------------------------------------

### Business Travel Customers

```{r, box_2}
valueBox(
  ## data
  gauge_compute %>%
    ## filter
    filter(
      # condition
      travel_type == "Business Travel"
    ) %>%
    ## extract value
    pull(count),
  # caption
  caption = "Number of Business Travel Cusotmers",
  # icon
  icon = "fa-chart-bar"
)
```

### Average Recommendation of Business Travel Customers

```{r, gauge_2}
### gauge
## call function
gauge(
  ## data
  gauge_compute %>%
    ## filter
    filter(
      # condition
      travel_type == "Business Travel"
    ) %>%
    ## extract value
    pull(mean_recommend),
  ## minimum and maximum
  min = 0, max = 100,
  ## sectors
  sectors = gaugeSectors(
    # green
    success = c(75, 100),
    # orange
    warning = c(50, 74),
    # red
    danger = c(0, 49)
  )
)
```